class_docstr: Sharding spec for single dimension of the sharded tensor describe the sharding dimension of
  logical device mesh and give a method to compute the difference between them.
  This class is used internally in ShardingSpec.
  
  Argument:
      shard_list(List[int]): if shard_list is None, the dim spec will be 'R' type.
          Otherwise, the element in shard_list means the data will be sharded in that dimension.
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docstr: The difference between two _DimSpec.
  
  Argument:
      other(_DimSpec): the dim spec to compare with.
  
  Return:
      difference(int): the difference between two _DimSpec.
  
  Example:
      dim_spec = _DimSpec([0])
      other_dim_spec = _DimSpec([0, 1])
      print(dim_spec.difference(other_dim_spec))
  
  Output:
      5
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code: def dim_diff(self, other):
      """
      The difference between two _DimSpec.
  
      Argument:
          other(_DimSpec): the dim spec to compare with.
  
      Return:
          difference(int): the difference between two _DimSpec.
  
      Example:
          dim_spec = _DimSpec([0])
          other_dim_spec = _DimSpec([0, 1])
          print(dim_spec.difference(other_dim_spec))
  
      Output:
          5
      """
      difference = self.difference_dict[(str(self), str(other))]
      return difference
  
file_path: colossalai/colossalai/tensor/d_tensor/sharding_spec.py
imports: from copy import deepcopy
  from typing import Dict, List
  from ..utils import merge_same_dim_mesh_list
  from .misc import ShardingOutOfIndexError
label_desc: Use image node_id 6 for calling the DimSpec obj's underlying member method code with example usage: obj.dim_diff(other) and returns: difference